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RESEARCH ARTICLE
Application of a targeted-enrichment
methodology for full-genome sequencing of
Dengue 1-4, Chikungunya and Zika viruses
directly from patient samples
Uma Sangumathi Kamaraj1, Jun Hao Tan1,2, Ong Xin Mei1, Louise Pan1, Tanu Chawla1,
Anna Uehara1, Lin-Fa Wang1, Eng Eong Ooi1, Duane J. Gubler1, Hasitha Tissera3, Lee
Ching Ng4, Annelies Wilder-Smith5, Paola Florez de Sessions6, Timothy BarkhamID7,
Danielle E. Anderson1, October Michael SessionsID1,2,8*
1 Duke-NUS Medical School, Singapore, 2 National University of Singapore, Saw Swee Hock School of
Public Health, Singapore, 3 Ministry of Health, Epidemiology Unit, Colombo, Sri Lanka, 4 Environmental
Health Institute, Singapore, 5 Nanyang Technological University, Lee Kong Chian School of Medicine,
Singapore, 6 Genome Institute of Singapore, Singapore, 7 Tan Tock Seng Hospital, Singapore, 8 National
University of Singapore, Department of Pharmacy, Singapore
Abstract
The frequency of epidemics caused by Dengue viruses 1–4, Zika virus and Chikungunya
viruses have been on an upward trend in recent years driven primarily by uncontrolled
urbanization, mobility of human populations and geographical spread of their shared vec-
tors, Aedes aegypti and Aedes albopictus. Infections by these viruses present with similar
clinical manifestations making them challenging to diagnose; this is especially difficult in
regions of the world hyperendemic for these viruses. In this study, we present a targeted-
enrichment methodology to simultaneously sequence the complete viral genomes for each
of these viruses directly from clinical samples. Additionally, we have also developed a cus-
tomized computational tool (BaitMaker) to design these enrichment baits. This methodology
is robust in its ability to capture diverse sequences and is amenable to large-scale epidemio-
logical studies. We have applied this methodology to two large cohorts: a febrile study
based in Colombo, Sri Lanka taken during the 2009–2015 dengue epidemic (n = 170) and
another taken during the 2016 outbreak of Zika virus in Singapore (n = 162). Results from
these studies indicate that we were able to cover an average of 97.04% ± 0.67% of the full
viral genome from samples in these cohorts. We also show detection of one DENV3/ZIKV
co-infected patient where we recovered full genomes for both viruses.
Author summary
Dengue viruses 1–4 (DENV1-4), Zika virus (ZIKV) and Chikungunya virus (CHIKV) are
tropical and subtropical viruses that share a common arthropod vector, and have very
similar clinical presentations that are difficult to distinguish. With the recent outbreaks of
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007184 April 25, 2019 1 / 17
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OPEN ACCESS
Citation: Kamaraj US, Tan JH, Xin Mei O, Pan L,
Chawla T, Uehara A, et al. (2019) Application of a
targeted-enrichment methodology for full-genome
sequencing of Dengue 1-4, Chikungunya and Zika
viruses directly from patient samples. PLoS Negl
Trop Dis 13(4): e0007184. https://doi.org/10.1371/
journal.pntd.0007184
Editor: William B. Messer, Oregon Health and
Science University, UNITED STATES
Received: September 19, 2018
Accepted: January 23, 2019
Published: April 25, 2019
Copyright: © 2019 Kamaraj et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
Funding: We would also like to thank the
Singapore National Medical Research Council
(http://www.nmrc.gov.sg) for supporting this study
through grants to OMS (NMRC/BNIG/2014/2014)
and EEO (CIRG13may040), the Singapore Ministry
of Education (https://www.moe.gov.sg) for a
Start Up grant to OMS and the European Union
DENV, ZIKV and CHIKV globally, a single methodology able to simultaneously distin-
guish these viruses and provide full-genome information would greatly increase our
capacity to rapidly characterize outbreaks. As a proof of principle, we have applied this
methodology to two large cohorts in Sri Lanka and Singapore taken during recent dengue
and Zika outbreaks, respectively. Herein, we present the results of this application to these
cohorts and provide the tools to replicate these methodologies for other cohorts.
Introduction
Dengue viruses 1–4 (DENV1-4), Zika virus (ZIKV) and Chikungunya virus (CHIKV) are
viruses spread by the Aedes aegypti and Aedes albopictus and are among the foremost arboviral
threats to humans today [1]. DENV and ZIKV are flaviviruses with positive-sense, single-
stranded RNA genomes of ~11 kb that encode for a single polyprotein, which is then post-
translationally cleaved into three structural proteins (C, prM and E) and seven non-structural
proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5) [2,3]. There are four different
DENV serotypes (DENV1-4) that share 60–75% identity at the amino acid level. Collectively,
DENV1-4 are responsible for an estimated 390 million infections yearly [4]. Evidence suggests
that the geographic distribution of DENV is increasing [5] and poses a threat to travelers visit-
ing affected regions [6].There is a single serotype of ZIKV that can be further broken down
into two primary lineages, the African and the Asian/American. At the time of writing, ZIKV
had spread to 86 countries [7], and is a documented cause of microcephaly in infants when
transmitted vertically from an infected mother during pregnancy [8]. CHIKV is an alphavirus
with three major lineages, all of which comprise a single serotype [9]. The genome of CHIKV
is a positive-sense, single-stranded RNA genome of ~11.6 kb that is post-translationally
cleaved into four nonstructural proteins (nsP1, nsP2, nsP3 and nsP4) and five structural pro-
teins (C, E3, E2, 6k and E1) [9]. CHIKV has seen resurgence in the tropical and subtropical
world with several notable epidemics in recent years [10].
Clinical presentation of infection with any of these six viruses is often similar; undifferenti-
ated fever, headache, nausea/vomiting, persistent myalgia/arthralgia, and rash [11]. Although
PCR assays that can discriminate between these viruses exist, they are often not used in con-
junction and are reliant on short primer sequences designed to target relatively conserved
regions of the viral genomes [12–15]. As these viruses all share a common replication strategy
dependent on error-prone RNA-dependent RNA polymerases, rapid mutation necessitates the
constant optimization of molecular diagnostic protocols for their accurate detection [12]. Fur-
ther, these methodologies are limited in that they provide no information on the particular
strain infecting the individual [12–15]. Recent studies have shown that polymorphisms in the
viral genome can have profound effects on the pathogenicity and epidemic potential of the
virus [16–22]. In response to these shortcomings, next-generation sequencing (NGS) is
increasingly being used as a tool to obtain the full genome sequence of viruses in clinical sam-
ples [23]. However, the principle drawback to this methodology is the often-overwhelming
amount of host material present in a clinical sample relative to the virus. In order to produce
sufficient data for full viral genome assembly from these clinical samples, only a small number
of samples can typically be run per lane of sequencing making this approach prohibitively
expensive for many laboratories. To overcome the inherent limitations of this direct sequenc-
ing approach, a targeted enrichment approach to increase the sensitivity and efficiency of
whole genome sequencing has been described for several pathogens of clinical importance
[24–26]. One limitation of this approach is the high upfront cost associated with the
Targeted-enrichment sequencing for Dengue, Chikungunya and Zika viruses in patient samples
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(https://cordis.europa.eu/result/rcn/189834_en.
html) for the grant to HT and AWS (“DengueTools”
- 282589). The funders had no role in study design,
data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
enrichment baits. In the current study, we present a novel computation method, BaitMaker, to
design baits that target the conserved and variable regions of a viral genome. The delineation of
the conserved and variable regions is made possible by employing a computationally efficient k-
mer based clustering approach on the available genetic information for DENV1-4, ZIKV and
CHIKV in the NCBI database. We have then applied our methodology to two large cohorts: a
collection of blood samples from the 2012–2015 DENV epidemic in Sri Lanka (n = 170) and
samples collected during the 2016 outbreak of ZIKV in Singapore (n = 162) [27,28].
Results
BaitMaker: An algorithm to design minimal baits for targeted enrichment
method
Targeted viral genome enrichment followed by high-throughput sequencing is an approach to
enrich viral genomes present in meager quantities in clinical samples. The targeted-enrich-
ment methodology uses biotinylated DNA baits, 120 nucleotides (nt) in length that are com-
plementary to the viral genome. Commonly used algorithms to design baits generate tiled,
overlapping baits across conserved genomic regions selected by multiple sequence alignment
[24,29–32]. While effective, these methodologies can generate redundant and overlapping
baits, which serves to increase the cost of the methodology in practice. In order to minimize
the number of baits necessary to capture a target viral genome, we developed a new computa-
tional method called BaitMaker. BaitMaker generates non-overlapping baits at an interval of
500 nt in the viral genome. This interval was chosen under the assumption that an average
deep sequencing library size ~300 nt and one bait can pull down two overlapping 300 nt DNA
fragments. Hence, our approach differs from similar methodologies where placements of baits
are tiled across the target genome [24,29–32] and allows for far fewer baits to be designed for
each virus. In order to ameliorate the potential impact of reducing the number of baits avail-
able for capturing a targeted viral genome, BaitMaker incorporates a k-mer based pattern
search and clustering strategy against a viral strain database (e.g. NCBI) to identify both con-
served and diverse regions in the viral genome. BaitMaker then utilizes this information in one
of two modes to design baits: (i) In ‘Conserved mode’, BaitMaker designs baits targeted to the
species-level conserved regions whereas in (ii) ‘Exhaustive mode’, BaitMaker designs baits for
both the conserved regions as well as regions with strain level variations. (Fig 1 and Methods).
The source code for BaitMaker method is freely made available at GitHub: https://
umasangumathi.github.io/BaitMaker/
For DENV serotypes 1, 2, 3 and 4, there were more than 11,000 genome sequences available
in the NCBI database (S1 Table). Using BaitMaker-Conserved mode we generated conserved
baits for each DENV serotype (65 baits total). As these baits are not designed to cover the
genomic regions that are more variable, we designed an additional 22 baits that were specific
to the Asian strains of DENV1-4 we were working with at the time (S2 Table). For ZIKV and
CHIKV, there were fewer available sequences in the NCBI database at the time of design (238
and 1260, respectively) (S1 Table) hence, we developed the BaitMaker Exhaustive mode and
utilized it to account for potentially under-represented variation in these viruses. The resulting
panels for these viruses were 67 baits for ZIKV and 53 baits for CHIKV covering both con-
served and variable regions of the respective genomes (S2 Table).
Specificity and sensitivity of the targeted viral enrichment methodology
To create a virus capture panel targeting DENV1-4, CHIKV and ZIKV simultaneously, we
pooled the 87 baits (65 targeting the conserved regions, 22 targeting the variable regions)
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specific to DENV1-4, the 67 baits specific to ZIKV and the 53 baits specific to CHIKV for a
total of 207 baits. To assess whether we could effectively capture the targeted viral genomes in
the context of high host background, we infected HuH7 cells with DENV1, 2, 3 or 4, Vero cells
with ZIKV and BHK-21 cells with CHIKV. To simulate a higher level of host contamination
that would potentially be found in clinical samples, the supernatant was discarded from these
cultures and total RNA was extracted from the infected cell monolayers and Illumina libraries
were constructed from the total RNA. These libraries were then divided in half where the first
half was sequenced directly, and the second half was enriched with our bait panel prior to
sequencing. The resulting sequencing reads from both conditions were then mapped against
the reference genome for each respective strain. Overall, we observed an average 2-log increase
in percentage of sequencing reads mapped to the viral genome following enrichment in the
DENV1-4, CHIKV and ZIKV samples (Fig 2, S1 Fig and S3 Table).
In order to assess the sensitivity of the assay, a 1:4 serial dilution of total RNA extracted
from ZIKV infected Vero cells was prepared. In order to keep the level of host RNA constant,
RNA extracted from uninfected Vero cells was used as the diluent. An approximate increase of
two Ct values for each successive dilution with a range from 29.79 to 41.08 was observed for
threshold detection. Libraries from these dilutions were then enriched and sequenced (S3
Table). Our results indicate that as the Ct value increases, the depth of coverage decreases with
respect to the breadth of genome coverage (Fig 3). In the sample with most virus (Ct value
29.79), 90% of the genome was covered with>4500x reads per genomic position. In the next
dilution (31.57 Ct), this decreases to an average coverage of ~1000x reads per genomic posi-
tion. Importantly, genome coverage at these depths is generally sufficient to assess inter/intra-
host diversity in the virus population [33]. We also observe that the number of low frequency
variants detected decreases as the dilution increases (S3 Table). At further dilutions (Ct values
Fig 1. Overview of BaitMaker. BaitMaker has two modes, Conserved and Exhaustive mode to design 120 nucleotide (nt) baits for targeted genome enrichment
methodology. (i) Conserved Mode: To generate conserved baits, complete or nearly complete viral genomes sequences were download from NCBI database. The mode
generates baits for virus species at the region that is most conserved at species-level and present across the different strains. These conserved baits were identified by
k-mer based search and clustering algorithm (PriMux) with the allowed number of six mismatches for bait hybridization. The baits were prioritized if the bait can target
at least 70% of the sequences from NCBI. Assuming the sequencing library size is 300 nt, the overlapping baits within 500 nt were removed to get non-redundant
conserved baits. For DENV, 65 conserved baits were generated. (ii) Exhaustive Mode: To generate exhaustive baits, viral sequences greater than 1000 nt were download
from NCBI database. This mode generates all possible baits targeting all the sequences in the viral database and thus contains baits targeting all the different viral strains.
Similar to the conserved mode, the overlapping baits and baits within 500 nt were removed. Then iteratively new baits were designed for the regions with no baits giving
an exhaustive list of baits targeting all the genomic variation across different strains.
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33.45 and above), the average depth of coverage which covers 90% of the genome drops below
~100x. Although this level of coverage is generally considered to be insufficient for the estima-
tion of viral subpopulations [33,34], consensus genomes can still be constructed and used for
phylogenetic analyses.
Application to clinical samples and the detection of viral co-infection
To measure the efficacy of the bait panel in clinical samples, we first tested the protocol on
clinical samples representative of each of the different serotypes of DENV. These four samples
were collected from DENV-infected patients who were enrolled in the early dengue infection
and outcome (EDEN) study in Singapore [35,36] and from the DengueTools study in Sri
Lanka [28]. The proportion of DENV reads relative to the host genome for different samples
varied from 0.10% to 90.7%. After enrichment, the proportion of DENV-specific reads was
between 94% and 99.6% for all serotypes tested. [Table 1 and S3 Table]. The most successful
enrichment occurred in the DENV4 sample, where only 0.10% DENV4 specific reads were
Fig 2. Genome coverage plots of unenriched and enriched samples of DENV1, ZIKV and CHIKV. The top panel (A, C, E) are unenriched samples whereas the
bottom panel (B, D, F) are matched enriched samples with baits. From the outermost circle, each plot reads as the viral genes in the genome, SNPs (single nucleotide
polymorphisms) detected, depth of coverage at each position in log scale shown in red and the baits hybridizing to the genome with varying sequence identity (80–
85% identity in blue, 85–90% in dark blue, 90–95% in green and 95–100% in dark green). The number within the circle indicates the percentage of sequencing reads
mapped to the genome.
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present in the unenriched library. Following enrichment, the number of DENV4 specific reads
was increased almost 3000-fold to 94% DENV4 specific sequences.
We then applied the bait panel to two clinical cohorts; one from Sri Lanka and another
from Singapore. The first cohort was collected as a laboratory-based enhanced sentinel surveil-
lance system in the Colombo District of Sri Lanka from 2012–2014 [28]. This collection period
coincided with a severe dengue epidemic predominantly (~80%) driven by DENV1 with a
smaller number (~20%) of cases due to DENV4 [28,37,38]. From this collection, we utilized
our enrichment platform to obtain full or nearly full genome sequences for 143 DENV1 and
27 DENV4 isolates (S2 Fig). The second cohort of 162 samples was collected from August to
September 2016 as part of a national response effort to the first outbreak of ZIKV in Singapore
(S2 Fig) [27,39]. During the course of our investigation, we were also able to detect one dual
Fig 3. Effect of viral titers on the depth of coverage and breadth of genome covered in ZIKV dilution series. Ct indicates ZIKV viral titers of the samples. The x-axis
represents the depth of coverage in log scale and y-axis represents the corresponding percentage of genome covered at the given coverage depth. The depth of coverage at
90% genome covered is discussed in the main text.
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Table 1. Sequencing information of unenriched and enriched clinical samples with DENV infection.
Sample
number
Clinical virus
sample
Unenriched sample (% of reads
mapped)
Enriched sample (% of reads
mapped)
Log fold
enrichment
Virus strain and
NCBI sequence ID
1 05K4172 EDEN
DENV1
77.45 99.6 0.11 DENV type 1 strain D1/SG/05K4172DK1/
2005 (EU081261.1)
2 06K2352 EDEN
DENV2
90.71 99.53 0.04 DENV2 strain BA05i (AY858035.2)
3 05K4176 EDEN
DENV3
31.03 98.2 0.5 DENV type 3 strain D3/SG/05K4176DK1/
2005 (EU081219.1)
4 SL558 Sri Lanka
DENV4
0.10 94.18 2.98 DENV4 isolate DENV4/IND/0952326/2009
(JQ922560.1)
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infection in the Singaporean cohort where we were able to recover both DENV3 and ZIKV
from the same patient (Fig 4).
To further examine the assumptions made by our method BaitMaker to generate baits, we
investigated the baits’ features such as GC content, melting temperature, Gibbs free energy and
sequence identity of the bait with the target genome and its effects on bait and target genome
hybridization. We tested whether the baits’ features affected the baits’ pull-down efficiency,
which is measured by the distribution of sequencing reads mapped to the genome around the
bait. Based on the principal component analysis on the baits’ features and pull-down efficiency
(S3 Fig) and effects of sequence identity on pull-down efficiency (S4 Fig), we observed that only
sequence identity had an influence on pull-down efficiency and this efficiency decreases as the
sequence identity between bait and target genome decreases. We next tested the effect of library
size on the genome enrichment of the samples in both DENV and ZIKV cohorts (S5 Fig). We
found that when the library size exceeds 300 nt, there is no further increase in coverage and this
is expected as the baits were designed for library size of 300 nt. This further suggests that for
larger library size, we can increase the bait design interval and thus only a smaller number of
Fig 4. Co-infection of DENV3 and ZIKV identified in one clinical subject by using the DENV, CHIKV and ZIKV
baits panel. From the outermost circle the plot reads as the DENV3 and ZIKV viral genomes, corresponding depth of
coverage and the locations where the baits hybridize.
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baits would be required to capture complete genomes. Thus, BaitMaker can efficiently design a
set of minimal number of baits which is required to capture a complete genome.
Discussion
DENV, ZIKV and CHIKV together represent the most significant arboviral threats to humans
today with hundreds of millions of infections annually. These viruses are predominantly trans-
mitted to humans by Aedes aegypti and Aedes albopictus species of mosquitoes whose global
distribution has exploded in recent years, placing an estimated two-thirds of the world’s popu-
lation at risk from contracting these diseases [4]. Calculating the true burden of disease for
each of these viruses is complicated by not only overlapping clinical presentations but impor-
tantly by the fact that they co-circulate [11]. Given the commonality of the arthropod vectors
these viruses employ for their transmission, the contribution of co-infection with these viruses
to morbidity and mortality is poorly understood.
Full-genome sequencing of DENV1-4, ZIKV and CHIKV directly from clinical samples is
not a routine practice in clinical, or even research, settings due to the costs associated with
labor, reagents and time. Additionally, given the low success rate of current methodologies,
loss of precious samples is a strong deterrent. Other groups have utilized a tiling approach
wherein baits are designed to cover the entire length of a limited number of target genomes
[24,29–32]. Although effective, we show here that this strategy represents an over-allocation of
baits and is unnecessary to obtain full genome coverage of the target virus. Our BaitMaker pro-
gram designs a minimal number of baits for viral capture that are non-overlapping and has the
ability to capture the known variation in viral strains. Our approach significantly reduces the
overall number of baits necessary to capture the diversity in these viruses which in turn mini-
mizes the cost incurred and should assist in the broader implementation of this methodology.
Although we have currently only tested our methodology with Illumina sequencing, we
foresee extending this methodology onto other sequencing platforms. Indeed, this methodol-
ogy may have an even larger impact for the so called ‘third generation’ sequencing platforms
(Pacbio and Nanopore) where average library sizes are far longer than Illumina but do not
yield as many reads per run. Increasing the average library size would presumably translate to
an even smaller number of baits required to capture full or near full genome coverage of the
target virus as our results have shown strong positive correlation between library size and
breadth of genome coverage (S5 Fig).
One particularly important benefit of utilizing a targeted enrichment approach over a
Sanger sequencing or an unbiased metagenomic approach, is the number of reads specific to
the viral genome are substantially increased and thus allows for assessment of intra-host
genetic diversity. These data are particularly important for the analysis of low-frequency vari-
ants that may represent precursors to a change in overall pathogenicity [16,17]. As vaccines for
these viruses are now in various stages of development, in-depth analysis of viral sub-popula-
tions will be critical to monitor vaccine escape mutants as they develop.
In this study, we have applied this methodology to a febrile cohort (n = 170) collected dur-
ing a severe dengue outbreak in Sri Lanka and to a cohort (n = 162) collected during the 2016
Zika outbreak in Singapore. We were able to obtain full or nearly full genomes for the target
viruses down to the limit of detection by qPCR. Importantly, we have combined bait panels for
all four DENVs, ZIKV and CHIKV into a single assay and have detected a co-infection in a
patient sample. Given the relatively small number of samples we have tested here and the over-
lapping clinical presentation of these viruses, the detection of a DENV/ZIKV co-infection indi-
cates that the global prevalence of ZIKV and CHIKV could be higher than current estimates.
Clinical management for DENV, ZIKV and CHIKV is largely supportive in nature with the
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vast majority of cases treated as outpatients and left to convalesce outside a clinical setting.
Whether co-infection with these viruses is a predicator of adverse clinical outcome is largely
understudied but it is a significant question that could potentially change clinical management
and outcome for some of these patients [40]. In the one sample where we identified co-infec-
tion, the amount of DENV3 was much greater than ZIKV. Whether this result is due to differ-
ential viral kinetics, viral interference or temporal differences in the acquisition of each virus is
an interesting question and is a subject of ongoing work.
Finally, we believe that application of the approach employed here for bait selection would
potentially improve upon the large, pan-viral enrichment panels such as those recently pub-
lished by Briese et al, 2017 and Wylie et al, 2015 [41,42]; with fewer baits required to achieve
full genome coverage, more baits could be allocated to capture the known diversity in the tar-
geted viral families. Increasing the amount of diversity in these panels would in turn increase
the likelihood of capturing novel viruses in clinical and environmental samples.
Materials and methods
Ethics statement
Investigations described for the Zika samples were conducted as part of outbreak response
operations by the Ministry of Health, Singapore to control the spread of Zika. Samples were
taken opportunistically with verbal informed consent from subjects. Approval for the collec-
tion of dengue samples was obtained from the Ethics Review Committee, Faculty of Medicine,
University of Colombo, Sri Lanka and informed consent obtained from participants in written
format. Parental consent was obtained for study participants below 18 years of age. Data from
both studies was anonymized prior to analysis and all methods were performed in accordance
with the relevant guidelines and regulations.
BaitMaker: Bait design algorithm
For targeted-enrichment method, we designed reverse complementary DNA baits of 120 nt in
length, targeting the viral genome of interest. As the hybridization takes place at 65˚C, we
designed baits such that they had a melting temperature greater than this. The baits in our
panel had melting temperature ranging from 69 to 87˚C and GC content from 31 to 66%. We
developed two modes to design baits (i) Conserved mode, to design baits at the species-level
conserved regions and (ii) Exhaustive baits, to design baits for both conserved regions as well
as regions with strain level variations.
Conserved mode. We downloaded available DENV1-4 sequences using the keyword
search "txid12637 [Organism:exp] AND 1000:12000 [slen] NOT clone NOT cloning NOT vec-
tor NOT chimeric" and retrieved 11,152 sequences from NCBI. In order to retrieve full or
nearly full genomes, we sub-selected the sequences with sequence length greater than 3/4th of
the maximum sequence length found for DENV in NCBI database. Some of the sequences
deposited in NCBI are redundant and this may lead to overrepresentation of a specific
sequence in our sub selected dataset. Hence, in order to get a diverse set of sequences repre-
senting DENV, we selected the sequences with at least two different values out of the seven
metadata terms (sequence length, host, strain name, collection date, isolate name, country,
strain taxonomic ID) to design baits. We then designed baits targeting the conserved regions
using the PriMux software [43], a k-mer based search and clustering method to generate all
possible overlapping 120 mers with a one nucleotide sliding window for the set of sequences
within each DENV serotype. This k-mer approach ensures that for identical 120 mers present
in different genome sequences, these 120 mers are grouped and a common 120 nt bait is de-
signed to target them. As the hybridization process between the bait and viral DNA fragment
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is tolerant to mismatches, we permitted six mismatches out of 120 nt in the bait’s design. In
NCBI, sequences less than full-genome length represent most of the known virus diversity,
therefore, we prioritized the selection of the k-mers if it could target at least 70% of the NCBI
sequences with at most six mismatches (95% identity). To further reduce redundancy in our
bait design, we removed the 120 mers that overlapped or were within a 500 nt distance. This is
based on the assumption that with an average library size of 300–500 nt, a 120 nt bait would be
able to pull down at least two fragments of length 300 nt overhanging the 120 nt bait (300 nt
+ 300 nt -120 nt = 480 nt). Thereby, we designed 14 conserved baits for DENV1, 16 for
DENV2, 19 for DENV3 and 16 for DENV4. However, few regions in DENV genome were
highly variable, and therefore we designed 22 baits explicitly targeting a selected reference
genome from South East Asian DENV cohort.
Exhaustive mode. To capture regions of greater diversity in the viral strains that cannot
be captured by conserved baits, we developed “Exhaustive mode” to design baits targeting all
the sequences in NCBI. We used the virus name and a minimum sequence length of 1000 nt to
extract sequences from NCBI. In addition, we also removed sequence with these terms (clone,
cloning, vector or chimeric) in the sequences’ metadata to ensure that the baits are not
designed against vector sequence. For CHIKV and ZIKV, we designed baits such that they
would capture all the known variations present in the viral strains, inclusive of partial genome
sequences deposited in NCBI. The Exhaustive mode of BaitMaker is an iterative process; first,
120 nt baits complementary to genome spaced at a distance of 500 nt for each NCBI sequence
are generated. After the initial baits are designed, CD-HIT [44], is employed to cluster and
remove redundant baits. As hybridization remains efficient with even with a degree of mis-
matching between the bait and target sequences, we clustered the baits within a mismatch
threshold of 18 nucleotides (85% identity) and 12 nucleotides (90% identity) for CHIKV and
ZIKV, respectively. For remaining regions with no baits, the process was iteratively repeated
until all regions within each target sequence was covered. A final pass of CD-HIT is then run
to remove any redundancy in the bait panel.
In the last step of the algorithm, the baits are then checked for potential cross-hybridization
with human, bacterial and mouse genomes using Blast. Any baits that are predicted to bind to
host genomes were removed, and a new bait is selected for this region.
Viral culture and clinical sample preparation
DENV1-4 and ZIKV were cultured in 2x105 HuH7 and Vero cells, respectively, for 48 h or
until cytopathic effects were observed. CHIKV was cultured in 2x105 BHK21 cells for 20 h.
After incubation, supernatant was removed and cell layers were scrapped into 250 μl of sterile
PBS. RNA from serum and urine samples were extracted directly. All viral cultures and clinical
samples were handled in a Class II-A2 biosafety cabinet under BSL-2 conditions according to
national regulations pertaining to the handling of infectious agents.
RNA extraction
RNA extraction was done using TRIzol Reagent (Life Technologies) according to the manufac-
turer’s instructions. Briefly, 250 μL of sample was added to 750 μL of TRIzol Reagent in a
Phase Lock Gel Heavy 2 mL tube (5 PRIME) and incubated for 10 min at room temperature.
Following incubation, 200 μL of chloroform (Sigma-Aldrich) was added and the mix was incu-
bated for a subsequent 10 min. The sample was centrifuged for 5 min at 13,000 rpm. The aque-
ous phase was decanted into a new tube and 2 μL of Glycoblue Coprecipitant (ThermoFisher
Scientific) and 500 μL of 2-propanol (Merck) was added to precipitate the RNA. The sample
was centrifuged at 13,000 rpm for 15 min to obtain an RNA pellet. The pellet was washed with
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PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007184 April 25, 2019 10 / 17
500 μL of 75% ethanol (Merck), centrifuged for 3 min at 13,000 rpm, air-dried and resus-
pended in nuclease free H2O.
Real-time PCR
Quantitative PCR (qPCR) was performed according to established methodologies [13]. Briefly,
we used the QuantiTect Probe RT-PCR Kit (Qiagen) reagents and the CFX96 Real-Time System
(Bio-Rad) where each 25 μL PCR reaction contained 12.5 μL 2X QuantiTect PCR mastermix,
1 μL of each 10 mM primer, 0.5 μL 0.2 mM probe, 0.5 μL reverse transcriptase, 3 μL RNA tem-
plate and 6.5 μL H2O. Every PCR was performed as follows: reverse transcription at 50˚C for 30
min, initial PCR activation at 95˚C for 5 min and 45 amplification cycles consisting of a 95˚C
denaturation for 10 sec and a 60˚C annealing/extension for 30 sec. Sequences of primers and
probes are as follows; ZIKV-F: 5’- TGG TCA TGA TAC TGC TGA TTG C -3’, ZIKV-R; 5’-
CCT TCC ACA AAG TCC CTA TTG C -3’, ZIKV-probe5’- /56-FAM/CGG CAT ACA GCA
TCA GGT GCA TAG GAG /3BHQ_1/ -3’, Vero (African green monkey) GAPDH-F:50- GGG
TGT GAA CCA TGA GAA GTA T-30, GAPDH-R; 50- GAG TCC TTC CAC GAT ACC AAA
G-30 and GAPDH-probe: 5’- /5HEX/AC AAC AGC CTC AAG ATC GTC AGC A/3BHQ_1/
-3’. The relative amount of viral transcript to GAPDH was calculated using the 2-ΔΔCT method.
Data were expressed as fold change RNA compared to the control.
Preparation of Illumina DNA libraries from viral RNA
Illumina libraries were constructed from total RNA using NEBNext Ultra Directional RNA
Library Prep Kit for Illumina (New England Biolabs) in conjunction with NEBNext Multiplex
Oligos for Illumina (New England Biolabs) according to the manufacturer’s instructions with
minor modifications. Briefly, 5 μL of total RNA was added to first strand synthesis buffer and
random primers before incubating at 94˚C for 2 min in order to generate RNA fragments larger
than 500 nt. Following first strand and second strand cDNA synthesis, double-stranded cDNA
was purified using Mag-Bind RxnPure Plus beads (Omega Bio-Tek) and eluted in 60 μL nucle-
ase-free water. In order to obtain a library size between 400–600 nt, size selection of the libraries
was performed using Mag-Bind RxnPure Plus beads (Omega Bio-Tek) in a two-step selection,
by adding 35 μL, then subsequently 15 μL of beads to the reaction. The library was eluted in
20 μL nuclease-free water and amplified by PCR. Libraries were purified using the MinElute
PCR Purification Kit (Qiagen), eluted in 25 μL nuclease-free H2O and visualized on a 1.5% aga-
rose gel and quantified using a Bioanalyzer High Sensitivity DNA Assay (Agilent).
Enrichment of viral library
Targeted viral enrichment was achieved using custom designed biotinylated, 120mer xGen
Lockdown baits (Integrated DNA Technologies). Prior to capture of viral sequences, 1 μL each
of xGen universal blocking oligo TS-p5 and TS-p7 (Integrated DNA Technologies), matched
accordingly to the library index was added to 20 μL of library DNA and 0.5 μL of 5 μg Cot-1
DNA (Invitrogen) to block binding of baits to non-viral regions of library fragments. Blocked
libraries were ethanol precipitated and resuspended in 2.5 μL H2O, 3 μL Nimblegen hybridiza-
tion solution and 7.5 μL Niblegen 2X hybridization buffer (Roche). Following a 10 min incuba-
tion at room temperature, resuspended libraries were denatured at 95˚C for 10 min and
cooled on ice before the addition of the DENV, CHIKV and ZIKV bait pool. A total amount of
3 pmol of baits were added and hybridized to the libraries for 4 h at 65˚C. To capture virus spe-
cific library fragments, 100 μL magnetic M-270 streptavidin Dynabeads (Life Technologies)
were added to the hybridization reaction and the mix was incubated for a further 45 min at
65˚C, with shaking at 2000 rpm in a ThermoMixer C (Eppendorf). Streptavidin beads were
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PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007184 April 25, 2019 11 / 17
washed to remove unbound DNA using SeqCap EZ hybridization and wash kit (Roche)
according to the manufacturer’s instructions. A post-capture PCR amplification of 20 cycles
with P1 and P2 primers (Illumina) was performed and the enriched library was purified using
the MinElute PCR Purification Kit (Qiagen). The purified, enriched library was eluted in
25 μL nuclease-free H2O, visualized on a 1.5% agarose gel and quantified using a Bioanalyzer
High Sensitivity DNA Assay (Agilent). For the complete protocol, please see S1 File.
Analysis of sequencing data
Enriched and unenriched libraries were constructed and sequenced on an Illumina MiSeq (Duke-
NUS Genome Biology Facility, Singapore) and Illumina HiSeq 4000 (Genome Institute of Singa-
pore). FastQC [45] was used to confirm the quality of the reads generated, and Trim Galore [46]
was used to trim and filter the reads with a minimum quality cutoff of 20 and a minimum read
length of 35 nt. As the viral species and strain is unknown in most of the cases, it is necessary to
identify the nearest species and the strain present in the sample. Therefore, Blast toolkit [47] was
used to search the nearest hit in the NCBI nucleotide database for every read using the megablast
option. A metagenomic analysis software MEGAN [48] was used to cluster reads at the species
level to visualize. As Blast analysis is time-consuming, only a portion of the reads were used to
identify the species and strain. The species cluster with the maximum number of reads assigned
was selected as the initial reference strain and used to generate a consensus genome. The consen-
sus genome was generated by using bam2cons_iter.sh script from the ViPR pipeline [49]. The
bam2cons_iter.sh uses BWA [50] to perform iterative mapping of the reads to the reference
genome and a consensus is generated based on the maximum frequency of a nucleotide at a given
position. From the obtained consensus genome, the nearest NCBI hit is found and used as a refer-
ence genome to rerun the bam2cons_iter.sh script with default parameters. This iterative consen-
sus genome generation approach enables generation of a full genome consensus for the virus
present in the sample. For final mapping with BWA mem v0.7.5 aligner was used to map the
reads to the consensus reference genome and picard tools v1.95 [51] were used to mark PCR
duplicates. Base calibration and indel realignment was done by GATK v3.3 [52]. Single nucleotide
variants for each sample were detected using LoFreq2 software [33], which incorporates base-call
quality scores as error probabilities into its model to distinguish SNVs from the average sequenc-
ing error rate, and assigns a p-value to each position (Bonferroni-corrected p-value> 0.05).
LoFreq has previously been applied to DENV datasets, and its SNV predictions on these datasets
have been experimentally validated down to 0.5% allele frequency [33], hence we filtered the
SNPs with a threshold of coverage (>1000) and allele frequency (>0.5%). Finally, the genome
coverage graph along with the baits positions and SNP positions were plotted using Circos [53].
The Pearson product-moment correlation analysis between the mean library size and one-
standard deviation of Gaussian distribution was performed in R v3.3. The mean library size of
the sample was computed using Picard-tools. The average one-standard deviation of Gaussian
distribution per bait (>95% identity), was calculated by fitting a Gaussian distribution to the
genome coverage in a window of 480nt around the bait. Quickfold from the mfold [54] pack-
age was used to find the Gibbs free energy for DNA bait secondary structure formation at tem-
perature 65˚C, 1 mM Na and 0 mM Mg. The principle component analysis between the GC
content, melting temperature, identity and mean coverage at the region where the bait hybrid-
izes with the genome was carried out in R.
Supporting information
S1 Fig. Genome coverage plots of unenriched and enriched samples of DENV2, DENV3
and DENV4. The top panel (A, C, E) are unenriched samples whereas the bottom panel (B, D,
Targeted-enrichment sequencing for Dengue, Chikungunya and Zika viruses in patient samples
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007184 April 25, 2019 12 / 17
F) are matched enriched samples with baits. From the outermost circle, each plot reads as the
viral genes in the genome, SNPs (single nucleotide polymorphisms) detected, depth of cover-
age at each position in log scale shown in red and the baits hybridizing to the genome with
varying sequence identity (80–85% identity in blue, 85–90% in dark blue, 90–95% in green
and 95–100% in dark green). The number within the circle indicates the percentage of
sequencing reads mapped to the genome.
(TIF)
S2 Fig. The mean genomic coverage of the clinical samples enriched by the DENV, CHIKV
and ZIKV baits panel: A) 143 DENV1 samples, B) 27 DENV4 samples and C) 162 ZIKV sam-
ples. The standard error is represented as a lighter shade around the mean at each genomic
location.
(TIF)
S3 Fig. In order to ascertain if the other properties of the bait affected the affinity of the
baits and fragments, we performed a principle component analysis (PCA) with the baits’
GC content, melting temperature (Tm), Gibbs free energy, sequence identity between bait
and genome, and bait pull down efficiency measured by mean genome coverage. The sam-
ples from DENV1 (n = 143), DENV-4 (n = 27), and ZIKV (n = 162) cohort was used for this
analysis. The first PCA component contains baits‘ GC and Tm, which are highly correlated
(loading score of -0.659 and -0.663, respectively). The second PC component contains
sequence identity and mean genome coverage that are highly correlated (loading score of
0.723 and 0.673, respectively). This suggests that the bait’s pull-down efficacy in term of mean
genome coverage depends only on the bait’s sequence identity with the target genome.
(TIF)
S4 Fig. Stacked depth of coverage at regions were the baits hybridize with the genome. The
enriched samples used in Fig 2 (consisting of DENV1, 2, 3 and 4, CHIKV and ZIKV) was used
to group the baits and genome targets based on their nucleotide identity: A) >95% identity
(n = 111), B) 90–95% identity (n = 32), C) 85–90% identity (n = 10) and D) 80–85% identity
(n = 25). The x-axis represents a 480 nt window of the genome with the bait at the center and
the y-axis represents the stacked depth of coverage. Baits designed for DENV1-4 are repre-
sented as D1, D2, D3 and D4. The 95–100% identity group has a symmetric, Gaussian distribu-
tion when compared to the rest of the groups. In contrast, at 80–85% identity, the baits do not
as effectively pull down their target region which is evident by the skew in distribution away
from the center of the bait. This skew in the distribution of reads depth around the bait is likely
due to the influence of a neighboring bait with a higher percent identity to its target region.
For example, in the DENV2 sample at the genomic region between 8800 and 9100, the Bait-
018-D2 bait hybridizes with 97.5% identity and zika_bait-43 (bait designed for ZIKV) binds
with 81.2% identity and they overlap by 20 nt. Hence, for the 80–85% identity group, which
includes the DENV2 genomic region enriched by zika_bait-43, has a skew towards the neigh-
boring bait (Bait-018-D2) as it has higher binding identity. It should be noted however, that
although the ZIKV bait binds at a lower efficiency, it is contributing to the coverage of DENV.
(TIF)
S5 Fig. Effect of library size on genome enrichment by the baits panel. The x-axis is the
mean library size of the samples and y-axis represents the genome coverage distribution given
by 1-SD (standard deviation) of the sequencing reads distribution enriched around the bait.
The range of library sizes for DENV1, DENV4, and ZIKV cohort samples are between 80–374
nt (median 203), 38–379 nt (median 175) and 101–800 nt (median 329) respectively. The num-
ber of samples in each cohort is indicated by n. For each virus group (DENV1, DENV4 and
Targeted-enrichment sequencing for Dengue, Chikungunya and Zika viruses in patient samples
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007184 April 25, 2019 13 / 17
ZIKV), the Pearson’s product-moment correlation coefficient is calculated and denoted by r
and confidence interval around the correlation coefficient is represented by CI. For DENV1
and DENV4 samples, there is a very strong positive Pearson correlation between coverage dis-
tribution and library size as their library sizes were below 300 nt. In contrast, there is a weak
positive correlation in the Singapore ZIKV outbreak cohort samples due to the larger library
size and number of baits targeting ZIKV is relatively higher (at>85% identity, DENV1 = 22,
DENV4 = 20, ZIKV = 50). The black line represents the LOWESS (locally weighted scatterplot
smoothing) fitted to all the cohort samples. When the library size exceeds 300 nt, there is no fur-
ther increase in coverage. This is expected as the baits were designed for sample library of 300
nt. This further suggests that larger the library size, we can increase the bait design interval and
thus only a smaller number of baits will be required to capture full viral genomes efficiently.
(TIF)
S1 Table. Genomic sequences extracted from NCBI used to design baits.
(XLSX)
S2 Table. Properties of baits used in the experiments.
(XLSX)
S3 Table. Sequencing information in detail for unenriched and enriched experiment (Fig
1), the ZIKV dilution series experiment (Fig 2) and a DENV2 dilution series experiment
(Figure not shown).
(XLSX)
S1 File. Enrichment protocol used in experiments.
(DOCX)
Acknowledgments
We would like to thank the individuals at the Singapore Ministry of Health, the Genome Insti-
tute of Singapore, Tan Tock Seng Hospital, the Environmental Health Institute of Singapore,
the Saw See Hock School of Public Health, the Bioinformatics Institute of Singapore, the
National Public Health Laboratory of Singapore, and the Epidemiology Unit and Medical
Research Institute of the Ministry of Health of Sri Lanka for facilitating the provision of the
clinical samples in this study.
Author Contributions
Conceptualization: Eng Eong Ooi, Paola Florez de Sessions, Danielle E. Anderson, October
Michael Sessions.
Data curation: Uma Sangumathi Kamaraj, Jun Hao Tan, October Michael Sessions.
Formal analysis: Uma Sangumathi Kamaraj, Jun Hao Tan, Paola Florez de Sessions, Danielle
E. Anderson, October Michael Sessions.
Funding acquisition: Eng Eong Ooi, October Michael Sessions.
Investigation: Uma Sangumathi Kamaraj, Jun Hao Tan, Ong Xin Mei, Louise Pan, Tanu
Chawla, Anna Uehara, Paola Florez de Sessions, Danielle E. Anderson, October Michael
Sessions.
Methodology: Uma Sangumathi Kamaraj, Jun Hao Tan, Ong Xin Mei, Louise Pan, Tanu
Chawla, Danielle E. Anderson, October Michael Sessions.
Targeted-enrichment sequencing for Dengue, Chikungunya and Zika viruses in patient samples
PLOS Neglected Tropical Diseases | https://doi.org/10.1371/journal.pntd.0007184 April 25, 2019 14 / 17
Project administration: October Michael Sessions.
Resources: Lin-Fa Wang, Eng Eong Ooi, Duane J. Gubler, Hasitha Tissera, Lee Ching Ng,
Annelies Wilder-Smith, Paola Florez de Sessions, Timothy Barkham, Danielle E. Anderson,
October Michael Sessions.
Software: Uma Sangumathi Kamaraj, Jun Hao Tan, October Michael Sessions.
Supervision: October Michael Sessions.
Validation: October Michael Sessions.
Visualization: Jun Hao Tan, October Michael Sessions.
Writing – original draft: Uma Sangumathi Kamaraj, Jun Hao Tan, Eng Eong Ooi, Paola
Florez de Sessions, Timothy Barkham, Danielle E. Anderson, October Michael Sessions.
Writing – review & editing: Uma Sangumathi Kamaraj, Jun Hao Tan, Ong Xin Mei, Louise
Pan, Tanu Chawla, Anna Uehara, Lin-Fa Wang, Eng Eong Ooi, Duane J. Gubler, Hasitha
Tissera, Lee Ching Ng, Annelies Wilder-Smith, Paola Florez de Sessions, Timothy Bark-
ham, Danielle E. Anderson, October Michael Sessions.
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Targeted-enrichment sequencing for Dengue, Chikungunya and Zika viruses in patient samples
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